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Torch Trader: Developer Documentation

Welcome to the Torch Trader developer documentation! This guide provides comprehensive information on how to develop and extend the Torch Trader platform. It covers the platform's architecture, data structures, APIs, and customization options, along with step-by-step tutorials for common development tasks.

Table of Contents

  1. Introduction
  2. Platform Architecture
  3. API Reference
  4. Customization and Extension
  5. Tutorials
  6. Contributing to Torch Trader
  7. License and Copyright

1. Introduction

1.1 Platform Overview

Torch Trader is a high-performance trading platform that supports stocks and cryptocurrencies. It provides advanced analytics, backtesting, deep learning, and trading bot automation in a user-friendly environment. The platform is built using Python and PyTorch, with a web-based interface for ease of use.

1.2 Development Environment Setup

To set up the Torch Trader development environment, follow these steps:

  1. Install Python 3.7 or later from the official Python website.
  2. Install PyTorch by following the official installation guide. For optimal performance, consider installing the CUDA-enabled version if you have a compatible NVIDIA GPU.
  3. Clone the Torch Trader repository from the official GitHub page.
  4. Install the required Python packages by running the following command in the repository's root directory: 
    pip install -r requirements.txt

  5. To start the development server, run

    python run.py
    6. Open your web browser and navigate to the provided URL (usually http://localhost:5000/) to access the Torch Trader development interface.

2. Platform Architecture

2.1 Components and Modules

The Torch Trader platform is organized into several components and modules, including:

  • Data Collection: Responsible for gathering and storing historical and real-time market data from various stock and crypto markets.
  • Scripting: Provides a scripting environment for creating and customizing trading strategies using technical analysis indicators and signals.
  • Backtesting: Implements a backtesting engine for testing trading strategies against historical market data.
  • Optimization: Offers optimization and deep learning tools for improving trading strategies and machine learning models.
  • Trading Bots: Handles automated trading, order execution, and performance monitoring for trading strategies.

These components interact with each other to provide a seamless trading experience. The following sections describe the roles of each component in more detail.

2.2 Interactions and Data Flow

The interactions and data flow among the different components and modules of Torch Trader are as follows:

  1. Data Collection gathers market data from various sources, such as public APIs or paid data providers, and stores it in a local or cloud-based database for further analysis.
  2. Scripting enables users to create and customize trading strategies using the platform's built-in technical indicators or custom Python code. The module interacts with the Data Collection component to access historical market data and generate trading signals based on the user's strategy.
  3. Backtesting evaluates the performance of trading strategies against historical market data by simulating trades based on the signals generated by the Scripting component. It computes performance metrics and visualizes trade executions for further analysis.
  4. Optimization uses advanced algorithms and deep learning techniques to fine-tune trading strategies and improve their performance. The module interacts with the Backtesting component to test various strategy configurations and the Scripting component to access custom indicators and strategy code.
  5. Trading Bots automate the execution of trading strategies based on user-defined rules and configurations. The bots communicate with external trading platforms to execute orders, monitor open positions, and track overall performance.

3. API Reference

Torch Trader provides a comprehensive set of APIs to interact with its various components and modules. The API documentation is organized as follows:

Each API section includes detailed information on the available functions, classes, and data structures, along with example usage.

4. Customization and Extension

Torch Trader is designed to be highly customizable and extensible, allowing developers to add new features and functionality. The following sections describe common customization and extension tasks:

Refer to the linked sections for step-by-step tutorials and best practices for each task.

5. Tutorials

The Torch Trader developer documentation includes a set of tutorials to help you get started with various development tasks:

Each tutorial provides a detailed walkthrough of the development process, including code examples and tips for success.

6. Contributing to Torch Trader

We welcome contributions from the developer community! If you're interested in contributing to Torch Trader, please review our contribution guidelines and code of conduct for more information.

Torch Trader is released under the MIT License. Copyright (c) Torch Trader Project.